Pandas scattermatrix中的类标签

Question

之前已经问过这个问题,分散矩阵中的多个数据,但没有得到答案.

我想制作一个散点矩阵,就像在pandas文档中一样,但是对于不同的类使用不同颜色的标记.例如,我希望某些点以绿色显示,而其他点则以蓝色显示,具体取决于其中一列(或单独的列表)的值.

这是使用Iris数据集的示例.点的颜色代表虹膜的种类 - Setosa,Versicolor或Virginica.

pandas(或matplotlib)有办法制作这样的图表吗？

Answer 1

更新:此功能现在是最新版本的Seaborn.这是一个例子.

以下是我的权宜之计:

def factor_scatter_matrix(df, factor, palette=None):
    '''Create a scatter matrix of the variables in df, with differently colored
    points depending on the value of df[factor].
    inputs:
        df: pandas.DataFrame containing the columns to be plotted, as well 
            as factor.
        factor: string or pandas.Series. The column indicating which group 
            each row belongs to.
        palette: A list of hex codes, at least as long as the number of groups.
            If omitted, a predefined palette will be used, but it only includes
            9 groups.
    '''
    import matplotlib.colors
    import numpy as np
    from pandas.tools.plotting import scatter_matrix
    from scipy.stats import gaussian_kde

    if isinstance(factor, basestring):
        factor_name = factor #save off the name
        factor = df[factor] #extract column
        df = df.drop(factor_name,axis=1) # remove from df, so it 
        # doesn't get a row and col in the plot.

    classes = list(set(factor))

    if palette is None:
        palette = ['#e41a1c', '#377eb8', '#4eae4b', 
                   '#994fa1', '#ff8101', '#fdfc33', 
                   '#a8572c', '#f482be', '#999999']

    color_map = dict(zip(classes,palette))

    if len(classes) > len(palette):
        raise ValueError('''Too many groups for the number of colors provided.
We only have {} colors in the palette, but you have {}
groups.'''.format(len(palette), len(classes)))

    colors = factor.apply(lambda group: color_map[group])
    axarr = scatter_matrix(df,figsize=(10,10),marker='o',c=colors,diagonal=None)


    for rc in xrange(len(df.columns)):
        for group in classes:
            y = df[factor == group].icol(rc).values
            gkde = gaussian_kde(y)
            ind = np.linspace(y.min(), y.max(), 1000)
            axarr[rc][rc].plot(ind, gkde.evaluate(ind),c=color_map[group])

    return axarr, color_map

例如,我们将使用与此处提供的问题相同的数据集

>>> import pandas as pd
>>> iris = pd.read_csv('iris.csv')
>>> axarr, color_map = factor_scatter_matrix(iris,'Name')
>>> color_map
{'Iris-setosa': '#377eb8',
 'Iris-versicolor': '#4eae4b',
 'Iris-virginica': '#e41a1c'}

希望这有用!

Answer 2

你也可以从pandas调用scattermatrix,如下所示:

pd.scatter_matrix(df,color=colors)

与colors被的大小的列表len(df)的颜色含